Abstract
We present a novel extension of the Mumford–Shah functional that allows to incorporate statistical shape knowledge at the computational level of image segmentation. Our approach exhibits various favorable properties: non-local convergence, robustness against noise, and the ability to take into consideration both shape evidence in given image data and knowledge about learned shapes. In particular, the latter property distinguishes our approach from previous work on contour–evolution based image segmentation. Experimental results confirm these properties.
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Cremers, D., Schnörr, C., Weickert, J., Schellewald, C. (2000). Diffusion–Snakes Using Statistical Shape Knowledge. In: Sommer, G., Zeevi, Y.Y. (eds) Algebraic Frames for the Perception-Action Cycle. AFPAC 2000. Lecture Notes in Computer Science, vol 1888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10722492_11
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DOI: https://doi.org/10.1007/10722492_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41013-3
Online ISBN: 978-3-540-45260-7
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